In the field of structural engineering, neural networks are utilized to detect damage in offshore jacket platforms using a combination of modal analysis results. By analyzing the shape of natural frequencies and utilizing information from five modal results, damage identification indices are developed. This study investigates Structural Health Monitoring (SHM) strategies utilizing mode shape analysis and neural networks to detect failures within a multi-story structure. Various failure scenarios, including corrosion-induced damage and collisions, were analyzed across 52 structural members, with induced damage ranging from 0% to 20%.Results reveal the critical importance of floor levels: the fourth floor exhibited heightened accuracy in failure detection, particularly in columns and wind braces, crucial for corrosion protection. Notably, secondary beams consistently displayed higher precision in identifying failures compared to main beams. The neural network performance peaked at a mean squared error of 0.039206, validating its efficiency in detecting and allocating damage across different structural elements. Subsequent scenarios involving two members of the fourth floor demonstrated the adaptability of the approach in addressing diverse failure rates. This research underscores the efficacy of combining mode shape analysis and neural networks for robust SHM. It emphasizes the significance of floor levels and specific structural elements in enhancing failure detection accuracy, offering valuable insights for proactive maintenance and structural integrity preservation.